{"id":"W4387580440","doi":"10.1007/s10661-023-11896-1","title":"Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: a support vector approach","year":2023,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University; St. Mary's University","funders":"","keywords":"Structural basin; Subsidence; Vegetation (pathology); Groundwater-related subsidence; Interferometric synthetic aperture radar; Environmental science; Hydrology (agriculture); Mean squared error; Geology; Physical geography; Remote sensing; Synthetic aperture radar; Geomorphology; Geography; Geotechnical engineering; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009132411,0.0001709808,0.0002032387,0.0007430979,0.0002420585,0.0003927424,0.0003970513,0.0002498518,0.000388377],"category_scores_gemma":[0.0001802179,0.0001550882,0.0002411958,0.0008208127,0.0002048451,0.0003261813,0.0002037941,0.0001926846,0.00008374882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002505221,"about_ca_system_score_gemma":0.0004421599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02630978,"about_ca_topic_score_gemma":0.03988444,"domain_scores_codex":[0.9999245,0.000009760938,0.000005813379,0.00001496637,0.00002271977,0.00002206212],"domain_scores_gemma":[0.9998837,0.0000260517,0.00002540253,0.000008726774,0.00003885795,0.00001729323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004979011,0.0006709617,0.71464,0.0003003998,0.0003340618,0.003871939,0.001676813,0.1028826,0.06181762,0.001868411,0.002102683,0.1093367],"study_design_scores_gemma":[0.00001298452,0.0001459821,0.9070482,0.00001327528,0.00007033819,0.0003897162,0.001492854,0.08740819,0.002356069,0.0001944678,0.0008482136,0.00001983363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986457,0.00004523677,0.0004494293,0.00004028137,0.000003563754,0.000005239654,0.0001162028,0.00001799182,0.0006764372],"genre_scores_gemma":[0.9992182,0.00004021297,0.0004451618,0.000004165413,0.000002450659,0.000002463989,0.0001006659,0.000001684161,0.0001849643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02630978,"threshold_uncertainty_score":0.05231327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732359610632623,"score_gpt":0.2934769164177848,"score_spread":0.2761533203114586,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}